Module Review

This chapter looked at FME Performance and some of the techniques available to improve it

What You Should Have Learned from this Module

The following are key points to be learned from this session:


  • Feature Caching enables inspection of the data at any point in the translation
  • Bookmarks are good for planning a workspace and can be used to increase performance when caching features
  • Partial runs allow you to run sections of workspaces without running the entire thing.
  • Analyzing a log file helps to determine where performance improvements can be made
  • Performance is the measure of useful work done in a given time. Excess data and caching of data to disk are two factors that can impact performance greatly
  • Improve reading performance by reducing the amount of data being read
  • Improve writing performance by ordering the FME Writers correctly
  • Improve transformation performance by removing excess attributes and properly managing group-based transformers
  • Upload larger tasks to multiple engines on FME Server
  • Make use of all reader/writer parameters to improve database performance
  • Employ parallel processing to make FME multi-threaded
  • Use batch processing to use multiple FME engines with multiple datasets

FME Skills

  • The ability to design a workspace before starting
  • The ability to analyze and deconstruct an FME log file
  • The ability to remove no data from rasters
  • An understanding of potential methods for improving reader, writer, and transformer performance
  • The ability to use database parameters to improve performance
  • The ability to apply parallel processing in an FME workspace

Further Reading

For further reading why not browse articles tagged with Performance on our blog?

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